updated 2024-09-20
Version 2409? (Build 16.0.17928.20148)
###Go Horse Process (XGH)
Content :
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
Specifically from Rakuten TV (live.tv.rakuten.co.jp)
FMI: https://cdm-project.com/How-To/ & https://old.reddit.com/r/Piracy/comments/y30ffr/
| cat urls.txt | httpx -json -store-response -output httpx.json | |
| cat httpx.json | jq -r '"\(.stored_response_path) \(.path | ltrimstr("/"))"' | xargs -n 2 sh -c 'mkdir -p "$(dirname $2)" && cp $1 $2' sh |
| # Ralph Agent Loop Script for GitHub Copilot CLI (PowerShell) | |
| # Continuously runs Copilot CLI on a task until completion criteria is met | |
| # Usage: .\ralph-loop.ps1 <job-name> | |
| param( | |
| [Parameter(Mandatory=$true, Position=0)] | |
| [string]$JobName | |
| ) | |
| # Configuration (can be overridden via environment variables) |